Parallel WaveGAN implementation with Pytorch

This repository provides UNOFFICIAL Parallel WaveGAN implementation with Pytorch.

The goal of this repository is to provide the real-time neural vocoder which is compatible with ESPnet-TTS.
Audio samples and pretrained models will be available at our google drive.

Source of the figure: https://arxiv.org/pdf/1910.11480.pdf

Requirements

This repository is tested on Ubuntu 16.04 with a GPU Titan V.

• Python 3.6+
• Cuda 10.0
• CuDNN 7+

All of the codes are tested on Pytorch 1.0.1, 1.1, 1.2, and 1.3.

Setup

You can select the installation method from two alternatives.

A. Use pip

$ git clone https://github.com/kan-bayashi/ParallelWaveGAN.git
$ cd ParallelWaveGAN
$ pip install -e .

B. Make virtualenv

$ git clone https://github.com/kan-bayashi/ParallelWaveGAN.git
$ cd ParallelWaveGAN/tools
$ make
$ source venv/bin/activate

Run

This repository provides Kaldi-style recipes, as the same as ESPnet.
Currently, three recipes are supported.

• LJSpeech: English female speaker
• JSUT: Japanese female speaker
• CSMSC: Mandarin female speaker

To run the recipe, please follow the below instruction.

# Let us move on the recipe directory
$ cd egs/ljspeech/voc1

# Run the recipe from scratch
$ ./run.sh

# You can select the stage to start and stop
$ ./run.sh --stage 2 --stop_stage 2

All of the hyperparameters is written in a single yaml format configuration file.
Please check this example in ljspeech recipe.

The training is still on going. Please check the (kan-bayashi#1).

References

• Parallel WaveGAN
• r9y9/wavenet_vocoder
• LiyuanLucasLiu/RAdam

Acknowledgement

The author would like to thank Ryuichi Yamamoto (@r9y9) for his great repository, paper and valuable discussions.

Author

Tomoki Hayashi (@kan-bayashi)
E-mail: hayashi.tomoki<at>g.sp.m.is.nagoya-u.ac.jp